UniCA: Adapting Time Series Foundation Model to General Covariate-Aware Forecasting
This addresses the problem of integrating heterogeneous covariates into time series forecasting for real-world applications, representing an incremental improvement over existing TSFMs.
The paper tackles the limitation of Time Series Foundation Models (TSFMs) in handling diverse covariates like categorical variables and multimodal data by proposing UniCA, a framework that adapts TSFMs to general covariate-aware forecasting, achieving superior performance on multiple benchmarks.
Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their ability to handle general forecasting tasks involving diverse and often heterogeneous covariates--such as categorical variables and multimodal data (e.g., images, text)--which are typically task-specific and difficult to leverage during pretraining. To address this gap, we propose Unified Covariate Adaptation (UniCA), a framework to bridge TSFMs with general covariate-aware forecasting. UniCA first performs covariate homogenization to transform heterogeneous covariates into high-level homogeneous series representations and then fuses them via a unified attention-based fusion mechanism. UniCA is compatible and universal for adaptation with both homogeneous and heterogeneous covariates, incorporating extra covariate information while preserving the generalization ability of TSFMs.Extensive experiments on multiple unimodal and multimodal covariate-aware forecasting benchmarks demonstrate the superiority of UniCA, highlighting the promise of covariate-aware TSFM adaptation in real-world forecasting scenarios. Codes are released on https://github.com/hanlu-nju/UniCA.